Many computer vision applications leverage machine learning for object recognition purposes. However, there are various challenges associated with existing machine learning object recognition techniques. For example, a labor-intensive training process is typically required for every type of object or condition that needs to be recognized. Further, significant computing resources are often required to perform recognition accurately and timely, thus rendering many resource-constrained devices unsuitable for object recognition purposes.